Text sentiment classification for SNS-based marketing using domain sentiment dictionary

In this paper, we propose a new method of classifying the sentiment behind tweets that contains formal and informal vocabulary. Previous methods used only formal vocabulary to classify the sentiments behind the sentences. However, these methods are ineffective in classifying texts since internet users make sentences using informal vocabulary. In addition, we use emotion based vocabulary to classify the sentiment behind texts. Feature vectors extracted from the vocabulary are classified by Support Vector Machine (SVM). Our proposed method shows a strong performance in the classifying the emotion behind the text.